The response?exposure?dose paradigm and ?exposure escalation? dosing strategy to be utilized in GOLDILOKs Project 1 (Clinical) is highly dependent on the ability to individualize dosing and achieve the desired exposure at the level of the individual child. Controlling the dose?exposure relationship requires appropriate pharmacokinetic (PK) models that take into consideration factors contributing to the observed variability in exposure. Physiologically based pharmacokinetic (PBPK) models are of considerable interest in pediatrics due to their potential to take multiple factors into consideration at a systems level. These include developmental changes in tissue volumes and composition, organ blood flow, gastric acidity, intestinal transit time, protein binding, among others that occur during growth and development. In building and applying PBPK models, both ?uncertainty? ? potential vulnerabilities related to parameter estimates and implicit assumptions that may be based on limited data ? and ?variability? -- ontogeny, genetic variation and environmental factors ? need to be considered. In this translational project, we will address several challenges relevant to building individualized Genome- Ontogeny PK (iGO-PK) models to inform optimal ATX dosing for an individual patient within the GOLDILOKs initiative. Aim 1 applies short read and long read next generation sequencing technology to refine phenotype predictions within the CYP2D6 gene locus and to resolve long range haplotypes with a distal (112kb) regulatory single nucleotide polymorphism (SNP) and the CYP2D6 coding region. By determining the content and characterizing the ontogeny of microsomal protein per gram liver (MPPGL) in pediatric liver tissue, Aim 2 will reduce uncertainty in a critical scaling factor for in vitro-in vivo extrapolations that form the basis of PBPK models. In Aim 2 we also propose to use the Simcyp PBPK framework and our pediatric liver bank to create a group of iGO-PK ?virtual individuals?, each with their own unique genotype data, quantitative (proteomic) abundance values for >50 drug metabolizing enzymes and transporters and corresponding MPPGL scaling factor. We anticipate that optimized CYP2D6 phenotype prediction from genotype data and improved PBPK models will result in improved dosing algorithms for dose individualization in children.